Business Case: Aerofit - Descriptive Statistics & Probability

By Santanu Dash

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import math
from scipy.stats import norm, binom, geom, zscore, ttest_1samp, ttest_ind

sns.set_theme(style="darkgrid")

aerofit = pd.read_csv("./aerofit_treadmill.csv")
aerofit.head(5)

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles
0	KP281	18	Male	14	Single	3	4	29562	112
1	KP281	19	Male	15	Single	2	3	31836	75
2	KP281	19	Female	14	Partnered	4	3	30699	66
3	KP281	19	Male	12	Single	3	3	32973	85
4	KP281	20	Male	13	Partnered	4	2	35247	47

Provided Inputs

price = pd.DataFrame([["KP281", 1500], ["KP481", 1750], ["KP781", 2500]],columns=["Product", "Price"])
price

	Product	Price
0	KP281	1500
1	KP481	1750
2	KP781	2500

Exploring The Data Set

aerofit.shape

(180, 9)

aerofit.size

1620

aerofit = aerofit[['Product', 'Gender', 'MaritalStatus', 'Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']]

aerofit.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180 entries, 0 to 179
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Product        180 non-null    object
 1   Gender         180 non-null    object
 2   MaritalStatus  180 non-null    object
 3   Age            180 non-null    int64 
 4   Income         180 non-null    int64 
 5   Miles          180 non-null    int64 
 6   Education      180 non-null    int64 
 7   Fitness        180 non-null    int64 
 8   Usage          180 non-null    int64 
dtypes: int64(6), object(3)
memory usage: 12.8+ KB

Missing value detection & fill with relevent data.

aerofit.isnull().sum()

Product          0
Gender           0
MaritalStatus    0
Age              0
Income           0
Miles            0
Education        0
Fitness          0
Usage            0
dtype: int64

Check for Outliers

def check_outlier(df, x):
    Q1 = df[x].quantile(0.25)
    Q3 = df[x].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5*IQR
    upper = Q3 + 1.5*IQR
    lower_outlier = df[x][df[x] < lower]
    upper_outlier = df[x][df[x] > upper]

    return {
        'lower': {
            'list': lower_outlier,
            'length': len(lower_outlier)
        },
        'upper': {
            'list': upper_outlier,
            'length': len(upper_outlier)
        }}

for i in aerofit[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].columns:
    # print(i)
    outlier = check_outlier(aerofit, i)
    print("{} : ({}, {})".format(i, outlier['lower']['length'], outlier['upper']['length']))

Age : (0, 5)
Income : (0, 19)
Miles : (0, 13)
Education : (0, 4)
Fitness : (2, 0)
Usage : (0, 9)

Conversion of categorical attributes to 'category'.

aerofit['Product'] = aerofit['Product'].astype('category')
aerofit['Gender'] = aerofit['Gender'].astype('category')
aerofit['MaritalStatus'] = aerofit['MaritalStatus'].astype('category')
aerofit.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180 entries, 0 to 179
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   Product        180 non-null    category
 1   Gender         180 non-null    category
 2   MaritalStatus  180 non-null    category
 3   Age            180 non-null    int64   
 4   Income         180 non-null    int64   
 5   Miles          180 non-null    int64   
 6   Education      180 non-null    int64   
 7   Fitness        180 non-null    int64   
 8   Usage          180 non-null    int64   
dtypes: category(3), int64(6)
memory usage: 9.5 KB

Insight

• 'Product', 'Gender', 'MaritalStatus' are Converted as Type Category.

Consolidated Data

def aerofit_categorise(df):
    df["age_group"] = pd.cut(x=df['Age'], bins=[0,15,20,25,30,35,40,45,50],  
                    labels=["0-15", "15-20", "20-25", "25-30", "30-35", "35-40", "40-45", "45-50"])
    df['age_group'] = df['age_group'].astype('category')

    df["income_group"] = pd.cut(x=df['Income'], bins=[0,25000,35000,45000,55000,65000,75000,85000,95000,105000],  
                    labels=["0-25K", "25K-35K", "35K-45K", "45K-55K", "55K-65K", "65K-75K", "75K-85K", "85K-95K", "95K-105K"])
    df['income_group'] = df['income_group'].astype('category')

    df["miles_group"] = pd.cut(x=df['Miles'], bins=[0,20,50,80,110,140,170,200,230,260,290,320,350,380],  
                    labels=["0-20", "20-50", "50-80", "80-110", "110-140", "140-170", "170-200", "200-230", "230-260", "260-290", "290-320", "320-350", "350-380"])
    df['miles_group'] = df['miles_group'].astype('category')

    df['education_group'] = df['Education'].astype('category')
    df['fitness_group'] = df['Fitness'].astype('category')
    df['usage_group'] = df['Usage'].astype('category')

    return df

aerofit = aerofit_categorise(aerofit)
aerofit.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180 entries, 0 to 179
Data columns (total 15 columns):
 #   Column           Non-Null Count  Dtype   
---  ------           --------------  -----   
 0   Product          180 non-null    category
 1   Gender           180 non-null    category
 2   MaritalStatus    180 non-null    category
 3   Age              180 non-null    int64   
 4   Income           180 non-null    int64   
 5   Miles            180 non-null    int64   
 6   Education        180 non-null    int64   
 7   Fitness          180 non-null    int64   
 8   Usage            180 non-null    int64   
 9   age_group        180 non-null    category
 10  income_group     180 non-null    category
 11  miles_group      180 non-null    category
 12  education_group  180 non-null    category
 13  fitness_group    180 non-null    category
 14  usage_group      180 non-null    category
dtypes: category(9), int64(6)
memory usage: 12.7 KB

Statistical Summary

Descriptive Statistits

aerofit[['Product', 'Gender', 'MaritalStatus']].describe()

	Product	Gender	MaritalStatus
count	180	180	180
unique	3	2	2
top	KP281	Male	Partnered
freq	80	104	107

aerofit[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].describe()

	Age	Income	Miles	Education	Fitness	Usage
count	180.000000	180.000000	180.000000	180.000000	180.000000	180.000000
mean	28.788889	53719.577778	103.194444	15.572222	3.311111	3.455556
std	6.943498	16506.684226	51.863605	1.617055	0.958869	1.084797
min	18.000000	29562.000000	21.000000	12.000000	1.000000	2.000000
25%	24.000000	44058.750000	66.000000	14.000000	3.000000	3.000000
50%	26.000000	50596.500000	94.000000	16.000000	3.000000	3.000000
75%	33.000000	58668.000000	114.750000	16.000000	4.000000	4.000000
max	50.000000	104581.000000	360.000000	21.000000	5.000000	7.000000

aerofit[['age_group', 'income_group', 'miles_group', 'education_group', 'usage_group', 'fitness_group']].describe()

	age_group	income_group	miles_group	education_group	usage_group	fitness_group
count	180	180	180	180	180	180
unique	7	8	11	8	6	5
top	20-25	45K-55K	80-110	16	3	3
freq	69	77	66	85	69	97

Unique Count

aerofit.nunique()

Product             3
Gender              2
MaritalStatus       2
Age                32
Income             62
Miles              37
Education           8
Fitness             5
Usage               6
age_group           7
income_group        8
miles_group        11
education_group     8
fitness_group       5
usage_group         6
dtype: int64

Mean

aerofit[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].mean()

Age             28.788889
Income       53719.577778
Miles          103.194444
Education       15.572222
Fitness          3.311111
Usage            3.455556
dtype: float64

Median

aerofit[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].median()

Age             26.0
Income       50596.5
Miles           94.0
Education       16.0
Fitness          3.0
Usage            3.0
dtype: float64

Mode

for i in aerofit.columns:
  print(i,':',aerofit[i].mode()[0])

Product : KP281
Gender : Male
MaritalStatus : Partnered
Age : 25
Income : 45480
Miles : 85
Education : 16
Fitness : 3
Usage : 3
age_group : 20-25
income_group : 45K-55K
miles_group : 80-110
education_group : 16
fitness_group : 3
usage_group : 3

Uni Variate Analysis

Product

aerofit['Product'].unique()

['KP281', 'KP481', 'KP781']
Categories (3, object): ['KP281', 'KP481', 'KP781']

aerofit['Product'].value_counts()

KP281    80
KP481    60
KP781    40
Name: Product, dtype: int64

Statitical Analysis

aerofit['Product'].describe()

count       180
unique        3
top       KP281
freq         80
Name: Product, dtype: object

aerofit['Product'].mode()[0]

'KP281'

Find Probability

aerofit['Product'].value_counts(normalize=True)*100

KP281    44.444444
KP481    33.333333
KP781    22.222222
Name: Product, dtype: float64

Plot the Graph

plt.figure(figsize=(15,5)).suptitle("Aerofit Product Dashboard",fontsize=14)

plt.subplot(1, 2, 1)
plt.pie(aerofit['Product'].value_counts().values,labels = aerofit['Product'].value_counts().index,radius = 1.3,autopct = '%1.2f%%',) # type: ignore

plt.subplot(1, 2, 2)
sns.countplot(aerofit, x='Product')
plt.xlabel('Products', fontsize=12)
plt.ylabel('Number of Purchase', fontsize=12)

plt.show()

#

Insight

• There is only 3 type of Product. which are KP281, KP481 & KP781.
• There are 80 KP281 which is equivalent to 44.44%.
• There are 60 KP481 which is equivalent to 33.33%.
• There are 40 KP781 which is equivalent to 22.22%.

Gender

aerofit['Gender'].unique()

['Male', 'Female']
Categories (2, object): ['Female', 'Male']

aerofit['Gender'].value_counts()

Male      104
Female     76
Name: Gender, dtype: int64

aerofit['Gender'].value_counts(normalize=True)*100

Male      57.777778
Female    42.222222
Name: Gender, dtype: float64

Statitical Analysis

aerofit['Gender'].describe()

count      180
unique       2
top       Male
freq       104
Name: Gender, dtype: object

aerofit['Gender'].mode()[0]

'Male'

aerofit.groupby('Product')["Gender"].describe()

	count	unique	top	freq
Product
KP281	80	2	Female	40
KP481	60	2	Male	31
KP781	40	2	Male	33

Find Probability

Probability of a Product & Gender across all Combination "Product ∩ Gender"

pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
Gender
Female	22.222222	16.111111	3.888889	42.222222
Male	22.222222	17.222222	18.333333	57.777778
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Gender "Product | Gender"

pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
Gender
Female	52.631579	38.157895	9.210526
Male	38.461538	29.807692	31.730769
All	44.444444	33.333333	22.222222

Probability of Gender for given Product "Gender | Product"

pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
Gender
Female	50.0	48.333333	17.5	42.222222
Male	50.0	51.666667	82.5	57.777778

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Gender', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product ∩ Gender')

Descriptive Plot

plt.figure(figsize=(15,5)).suptitle("Aerofit Gender Dashboard",fontsize=14)

plt.subplot(1, 3, 1)
plt.pie(aerofit['Gender'].value_counts().values,labels = aerofit['Gender'].value_counts().index,radius = 1.3,autopct = '%1.2f%%',) # type: ignore

plt.subplot(1, 3, 2)
sns.countplot(aerofit, x='Gender')
plt.ylabel('', fontsize=12)
plt.xlabel('Gender', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(1, 3, 3)
sns.countplot(aerofit, x='Gender', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Gender', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=-1, ncol=3)

plt.show()

Probability Plot

(pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=0, ncol=3)
plt.show()

(pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=3)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=2)
plt.show()

Population Plot

pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(12,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()
pd.crosstab(aerofit['Product'], aerofit['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(8,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()

#

Insight

• There are 104 Male which is equivalent to 57.78%.
• There are 76 Female which is equivalent to 42.22%.

Marital Status

aerofit['MaritalStatus'].unique()

['Single', 'Partnered']
Categories (2, object): ['Partnered', 'Single']

aerofit['MaritalStatus'].value_counts()

Partnered    107
Single        73
Name: MaritalStatus, dtype: int64

aerofit['MaritalStatus'].value_counts(normalize=True)*100

Partnered    59.444444
Single       40.555556
Name: MaritalStatus, dtype: float64

Statitical Analysis

aerofit['MaritalStatus'].describe()

count           180
unique            2
top       Partnered
freq            107
Name: MaritalStatus, dtype: object

aerofit['MaritalStatus'].mode()[0]

'Partnered'

aerofit.groupby('Product')["MaritalStatus"].describe()

	count	unique	top	freq
Product
KP281	80	2	Partnered	48
KP481	60	2	Partnered	36
KP781	40	2	Partnered	23

Find Probability

Probability of a Product & Marital Status across all Combination "Product ∩ Marital Status"

pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
MaritalStatus
Partnered	26.666667	20.000000	12.777778	59.444444
Single	17.777778	13.333333	9.444444	40.555556
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Marital Status "Product | Marital Status"

pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
MaritalStatus
Partnered	44.859813	33.644860	21.495327
Single	43.835616	32.876712	23.287671
All	44.444444	33.333333	22.222222

Probability of Marital Status for given Product "Marital Status | Product"

pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
MaritalStatus
Partnered	60.0	60.0	57.5	59.444444
Single	40.0	40.0	42.5	40.555556

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['MaritalStatus'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Marital Status', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product ∩ Marital Status')

Descriptive Plot

plt.figure(figsize=(15,5)).suptitle("Aerofit MaritalStatus Dashboard",fontsize=14)

plt.subplot(1, 3, 1)
plt.pie(aerofit['MaritalStatus'].value_counts().values,labels = aerofit['MaritalStatus'].value_counts().index,radius = 1.3,autopct = '%1.2f%%',) # type: ignore

plt.subplot(1, 3, 2)
sns.countplot(aerofit, x='MaritalStatus')
plt.ylabel('', fontsize=12)
plt.xlabel('Marital Status', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(1, 3, 3)
sns.countplot(aerofit, x='MaritalStatus', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Marital Status', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=0, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Marital Status', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Marital Status', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=3)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['MaritalStatus'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Marital Status for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=3)
plt.show()

Population Plot

pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(12,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()
pd.crosstab(aerofit['Product'], aerofit['MaritalStatus'], normalize="index").plot(kind='pie', subplots=True, figsize=(8,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()

#

Insight

• There are 107 Partnered which is equivalent to 59.44%.
• There are 73 Single which is equivalent to 40.56%.

Age

Age value Count

aerofit['Age'].unique()

array([18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
       35, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 50, 45, 48, 42],
      dtype=int64)

aerofit['Age'].value_counts()[:5]

25    25
23    18
24    12
26    12
28     9
Name: Age, dtype: int64

aerofit['Age'].value_counts(normalize=True)[:5]*100

25    13.888889
23    10.000000
24     6.666667
26     6.666667
28     5.000000
Name: Age, dtype: float64

Age Group Value Count

aerofit['age_group'].unique()

['15-20', '20-25', '25-30', '30-35', '35-40', '40-45', '45-50']
Categories (8, object): ['0-15' < '15-20' < '20-25' < '25-30' < '30-35' < '35-40' < '40-45' < '45-50']

aerofit['age_group'].value_counts()[:5]

20-25    69
25-30    41
30-35    32
35-40    16
15-20    10
Name: age_group, dtype: int64

aerofit['age_group'].value_counts(normalize=True)[:5]*100

20-25    38.333333
25-30    22.777778
30-35    17.777778
35-40     8.888889
15-20     5.555556
Name: age_group, dtype: float64

Statitical Analysis

aerofit['Age'].describe()

count    180.000000
mean      28.788889
std        6.943498
min       18.000000
25%       24.000000
50%       26.000000
75%       33.000000
max       50.000000
Name: Age, dtype: float64

aerofit['age_group'].describe()

count       180
unique        7
top       20-25
freq         69
Name: age_group, dtype: object

aerofit['Age'].mean()

28.788888888888888

aerofit['Age'].median()

26.0

aerofit['Age'].mode()[0]

25

aerofit.groupby('Product')["Age"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	28.55	7.221452	18.0	23.00	26.0	33.00	50.0
KP481	60.0	28.90	6.645248	19.0	24.00	26.0	33.25	48.0
KP781	40.0	29.10	6.971738	22.0	24.75	27.0	30.25	48.0

aerofit.groupby('Product')["age_group"].describe()

	count	unique	top	freq
Product
KP281	80	7	20-25	28
KP481	60	7	20-25	24
KP781	40	6	20-25	17

Check for Outliers

check_outlier(aerofit, 'Age')['upper']

{'list': 78     47
 79     50
 139    48
 178    47
 179    48
 Name: Age, dtype: int64,
 'length': 5}

check_outlier(aerofit, 'Age')['lower']

{'list': Series([], Name: Age, dtype: int64), 'length': 0}

Find Probability

Probability of a Product & Age Group across all Combination "Product ∩ Age Group"

pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
age_group
15-20	3.333333	2.222222	0.000000	5.555556
20-25	15.555556	13.333333	9.444444	38.333333
25-30	11.666667	3.888889	7.222222	22.777778
30-35	6.111111	9.444444	2.222222	17.777778
35-40	4.444444	3.333333	1.111111	8.888889
40-45	1.666667	0.555556	1.111111	3.333333
45-50	1.666667	0.555556	1.111111	3.333333
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Age Group "Product | Age Group"

pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
age_group
15-20	60.000000	40.000000	0.000000
20-25	40.579710	34.782609	24.637681
25-30	51.219512	17.073171	31.707317
30-35	34.375000	53.125000	12.500000
35-40	50.000000	37.500000	12.500000
40-45	50.000000	16.666667	33.333333
45-50	50.000000	16.666667	33.333333
All	44.444444	33.333333	22.222222

Probability of Age Group for given Product "Age Group | Product"

pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
age_group
15-20	7.50	6.666667	0.0	5.555556
20-25	35.00	40.000000	42.5	38.333333
25-30	26.25	11.666667	32.5	22.777778
30-35	13.75	28.333333	10.0	17.777778
35-40	10.00	10.000000	5.0	8.888889
40-45	3.75	1.666667	5.0	3.333333
45-50	3.75	1.666667	5.0	3.333333

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['age_group'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Age', fontsize=12)
plt.show()

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Age Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['age_group'].value_counts().values,labels = aerofit['age_group'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Age", x='Product')

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Age', binwidth=1, kde=True, hue='Product')
plt.title('No of Product Purchase vs Age', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='age_group')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='age_group', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Age', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Age Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Age Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=3)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['age_group'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Age Group for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=6)
plt.show()

Income

Income value Count

aerofit['Income'].unique()

array([ 29562,  31836,  30699,  32973,  35247,  37521,  36384,  38658,
        40932,  34110,  39795,  42069,  44343,  45480,  46617,  48891,
        53439,  43206,  52302,  51165,  50028,  54576,  68220,  55713,
        60261,  67083,  56850,  59124,  61398,  57987,  64809,  47754,
        65220,  62535,  48658,  54781,  48556,  58516,  53536,  61006,
        57271,  52291,  49801,  62251,  64741,  70966,  75946,  74701,
        69721,  83416,  88396,  90886,  92131,  77191,  52290,  85906,
       103336,  99601,  89641,  95866, 104581,  95508], dtype=int64)

aerofit['Income'].value_counts()[:5]

45480    14
52302     9
46617     8
54576     8
53439     8
Name: Income, dtype: int64

aerofit['Income'].value_counts(normalize=True)[:5]*100

45480    7.777778
52302    5.000000
46617    4.444444
54576    4.444444
53439    4.444444
Name: Income, dtype: float64

Income Group Value Count

aerofit['income_group'].unique()

['25K-35K', '35K-45K', '45K-55K', '65K-75K', '55K-65K', '75K-85K', '85K-95K', '95K-105K']
Categories (9, object): ['0-25K' < '25K-35K' < '35K-45K' < '45K-55K' ... '65K-75K' < '75K-85K' < '85K-95K' < '95K-105K']

aerofit['income_group'].value_counts()[:5]

45K-55K    77
35K-45K    35
55K-65K    26
25K-35K    14
85K-95K    11
Name: income_group, dtype: int64

aerofit['income_group'].value_counts(normalize=True)[:5]*100

45K-55K    42.777778
35K-45K    19.444444
55K-65K    14.444444
25K-35K     7.777778
85K-95K     6.111111
Name: income_group, dtype: float64

Statitical Analysis

aerofit['Income'].describe()

count       180.000000
mean      53719.577778
std       16506.684226
min       29562.000000
25%       44058.750000
50%       50596.500000
75%       58668.000000
max      104581.000000
Name: Income, dtype: float64

aerofit['income_group'].describe()

count         180
unique          8
top       45K-55K
freq           77
Name: income_group, dtype: object

aerofit['Income'].mean()

53719.57777777778

aerofit['Income'].median()

50596.5

aerofit['Income'].mode()[0]

45480

aerofit.groupby('Product')["Income"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	46418.025	9075.783190	29562.0	38658.00	46617.0	53439.0	68220.0
KP481	60.0	48973.650	8653.989388	31836.0	44911.50	49459.5	53439.0	67083.0
KP781	40.0	75441.575	18505.836720	48556.0	58204.75	76568.5	90886.0	104581.0

aerofit.groupby('Product')["income_group"].describe()

	count	unique	top	freq
Product
KP281	80	5	45K-55K	35
KP481	60	5	45K-55K	33
KP781	40	6	85K-95K	11

Check for Outliers

check_outlier(aerofit, 'Income')['upper']

{'list': 159     83416
 160     88396
 161     90886
 162     92131
 164     88396
 166     85906
 167     90886
 168    103336
 169     99601
 170     89641
 171     95866
 172     92131
 173     92131
 174    104581
 175     83416
 176     89641
 177     90886
 178    104581
 179     95508
 Name: Income, dtype: int64,
 'length': 19}

check_outlier(aerofit, 'Income')['lower']

{'list': Series([], Name: Income, dtype: int64), 'length': 0}

Find Probability

Probability of a Product & Income Group across all Combination "Product ∩ Income Group"

pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
income_group
25K-35K	4.444444	3.333333	0.000000	7.777778
35K-45K	14.444444	5.000000	0.000000	19.444444
45K-55K	19.444444	18.333333	5.000000	42.777778
55K-65K	5.000000	5.555556	3.888889	14.444444
65K-75K	1.111111	1.111111	1.666667	3.888889
75K-85K	0.000000	0.000000	2.222222	2.222222
85K-95K	0.000000	0.000000	6.111111	6.111111
95K-105K	0.000000	0.000000	3.333333	3.333333
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Income Group "Product | Income Group"

pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
income_group
25K-35K	57.142857	42.857143	0.000000
35K-45K	74.285714	25.714286	0.000000
45K-55K	45.454545	42.857143	11.688312
55K-65K	34.615385	38.461538	26.923077
65K-75K	28.571429	28.571429	42.857143
75K-85K	0.000000	0.000000	100.000000
85K-95K	0.000000	0.000000	100.000000
95K-105K	0.000000	0.000000	100.000000
All	44.444444	33.333333	22.222222

Probability of Income Group for given Product "Income Group | Product"

pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
income_group
25K-35K	10.00	10.000000	0.0	7.777778
35K-45K	32.50	15.000000	0.0	19.444444
45K-55K	43.75	55.000000	22.5	42.777778
55K-65K	11.25	16.666667	17.5	14.444444
65K-75K	2.50	3.333333	7.5	3.888889
75K-85K	0.00	0.000000	10.0	2.222222
85K-95K	0.00	0.000000	27.5	6.111111
95K-105K	0.00	0.000000	15.0	3.333333

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['income_group'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Income', fontsize=12)
plt.show()

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Income Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['income_group'].value_counts().values,labels = aerofit['income_group'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Income", x='Product')

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Income', binwidth=1000, kde=True, hue='Product')
plt.title('No of Product Purchase vs Income', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='income_group')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='income_group', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Income', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Income Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Income Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=3)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['income_group'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Income Group for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=6)
plt.show()

Miles

Miles value Count

aerofit['Miles'].unique()

array([112,  75,  66,  85,  47, 141, 103,  94, 113,  38, 188,  56, 132,
       169,  64,  53, 106,  95, 212,  42, 127,  74, 170,  21, 120, 200,
       140, 100,  80, 160, 180, 240, 150, 300, 280, 260, 360], dtype=int64)

aerofit['Miles'].value_counts()[:5]

85    27
95    12
66    10
75    10
47     9
Name: Miles, dtype: int64

aerofit['Miles'].value_counts(normalize=True)[:5]*100

85    15.000000
95     6.666667
66     5.555556
75     5.555556
47     5.000000
Name: Miles, dtype: float64

Miles Group Value Count

aerofit['miles_group'].unique()

['110-140', '50-80', '80-110', '20-50', '140-170', ..., '200-230', '230-260', '290-320', '260-290', '350-380']
Length: 11
Categories (13, object): ['0-20' < '20-50' < '50-80' < '80-110' ... '260-290' < '290-320' < '320-350' < '350-380']

aerofit['miles_group'].value_counts()[:5]

80-110     66
50-80      43
110-140    20
20-50      17
140-170    15
Name: miles_group, dtype: int64

aerofit['miles_group'].value_counts(normalize=True)[:5]*100

80-110     36.666667
50-80      23.888889
110-140    11.111111
20-50       9.444444
140-170     8.333333
Name: miles_group, dtype: float64

Statitical Analysis

aerofit['Miles'].describe()

count    180.000000
mean     103.194444
std       51.863605
min       21.000000
25%       66.000000
50%       94.000000
75%      114.750000
max      360.000000
Name: Miles, dtype: float64

aerofit['miles_group'].describe()

count        180
unique        11
top       80-110
freq          66
Name: miles_group, dtype: object

aerofit['Miles'].mean()

103.19444444444444

aerofit['Miles'].median()

94.0

aerofit['Miles'].mode()[0]

85

aerofit.groupby('Product')["Miles"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	82.787500	28.874102	38.0	66.0	85.0	94.0	188.0
KP481	60.0	87.933333	33.263135	21.0	64.0	85.0	106.0	212.0
KP781	40.0	166.900000	60.066544	80.0	120.0	160.0	200.0	360.0

aerofit.groupby('Product')["miles_group"].describe()

	count	unique	top	freq
Product
KP281	80	6	80-110	27
KP481	60	6	80-110	31
KP781	40	9	170-200	12

Check for Outliers

check_outlier(aerofit, 'Miles')['upper']

{'list': 23     188
 84     212
 142    200
 148    200
 152    200
 155    240
 166    300
 167    280
 170    260
 171    200
 173    360
 175    200
 176    200
 Name: Miles, dtype: int64,
 'length': 13}

check_outlier(aerofit, 'Miles')['lower']

{'list': Series([], Name: Miles, dtype: int64), 'length': 0}

Find Probability

Probability of a Product & Miles Group across all Combination "Product ∩ Miles Group"

pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
miles_group
20-50	6.666667	2.777778	0.000000	9.444444
50-80	14.444444	8.888889	0.555556	23.888889
80-110	15.000000	17.222222	4.444444	36.666667
110-140	6.111111	2.777778	2.222222	11.111111
140-170	1.666667	1.111111	5.555556	8.333333
170-200	0.555556	0.000000	6.666667	7.222222
200-230	0.000000	0.555556	0.000000	0.555556
230-260	0.000000	0.000000	1.111111	1.111111
260-290	0.000000	0.000000	0.555556	0.555556
290-320	0.000000	0.000000	0.555556	0.555556
350-380	0.000000	0.000000	0.555556	0.555556
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Miles Group "Product | Miles Group"

pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
miles_group
20-50	70.588235	29.411765	0.000000
50-80	60.465116	37.209302	2.325581
80-110	40.909091	46.969697	12.121212
110-140	55.000000	25.000000	20.000000
140-170	20.000000	13.333333	66.666667
170-200	7.692308	0.000000	92.307692
200-230	0.000000	100.000000	0.000000
230-260	0.000000	0.000000	100.000000
260-290	0.000000	0.000000	100.000000
290-320	0.000000	0.000000	100.000000
350-380	0.000000	0.000000	100.000000
All	44.444444	33.333333	22.222222

Probability of Miles Group for given Product "Miles Group | Product"

pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
miles_group
20-50	15.00	8.333333	0.0	9.444444
50-80	32.50	26.666667	2.5	23.888889
80-110	33.75	51.666667	20.0	36.666667
110-140	13.75	8.333333	10.0	11.111111
140-170	3.75	3.333333	25.0	8.333333
170-200	1.25	0.000000	30.0	7.222222
200-230	0.00	1.666667	0.0	0.555556
230-260	0.00	0.000000	5.0	1.111111
260-290	0.00	0.000000	2.5	0.555556
290-320	0.00	0.000000	2.5	0.555556
350-380	0.00	0.000000	2.5	0.555556

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['miles_group'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Miles', fontsize=12)
plt.show()

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Miles Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['miles_group'].value_counts().values,labels = aerofit['miles_group'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Miles", x='Product')

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Miles', binwidth=10, kde=True, hue='Product')
plt.title('No of Product Purchase vs Miles', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='miles_group')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=8)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='miles_group', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Miles', fontsize=13)
plt.xticks(fontsize=8)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Miles Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=8)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Miles Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=8)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=3)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['miles_group'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Miles Group for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=6)
plt.show()

Education

aerofit['Education'].unique()

array([14, 15, 12, 13, 16, 18, 20, 21], dtype=int64)

aerofit['Education'].value_counts()

16    85
14    55
18    23
15     5
13     5
12     3
21     3
20     1
Name: Education, dtype: int64

aerofit['Education'].value_counts(normalize=True)*100

16    47.222222
14    30.555556
18    12.777778
15     2.777778
13     2.777778
12     1.666667
21     1.666667
20     0.555556
Name: Education, dtype: float64

Statitical Analysis

aerofit['Education'].describe()

count    180.000000
mean      15.572222
std        1.617055
min       12.000000
25%       14.000000
50%       16.000000
75%       16.000000
max       21.000000
Name: Education, dtype: float64

aerofit['Education'].mean()

15.572222222222223

aerofit['Education'].median()

16.0

aerofit['Education'].mode()[0]

16

aerofit.groupby('Product')["Education"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	15.037500	1.216383	12.0	14.0	16.0	16.0	18.0
KP481	60.0	15.116667	1.222552	12.0	14.0	16.0	16.0	18.0
KP781	40.0	17.325000	1.639066	14.0	16.0	18.0	18.0	21.0

Check for Outliers

check_outlier(aerofit, 'Education')['upper']

{'list': 156    20
 157    21
 161    21
 175    21
 Name: Education, dtype: int64,
 'length': 4}

check_outlier(aerofit, 'Education')['lower']

{'list': Series([], Name: Education, dtype: int64), 'length': 0}

Find Probability

Probability of a Product & Education across all Combination "Product ∩ Education"

pd.crosstab(aerofit['Education'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
Education
12	1.111111	0.555556	0.000000	1.666667
13	1.666667	1.111111	0.000000	2.777778
14	16.666667	12.777778	1.111111	30.555556
15	2.222222	0.555556	0.000000	2.777778
16	21.666667	17.222222	8.333333	47.222222
18	1.111111	1.111111	10.555556	12.777778
20	0.000000	0.000000	0.555556	0.555556
21	0.000000	0.000000	1.666667	1.666667
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Education "Product | Education"

pd.crosstab(aerofit['Education'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
Education
12	66.666667	33.333333	0.000000
13	60.000000	40.000000	0.000000
14	54.545455	41.818182	3.636364
15	80.000000	20.000000	0.000000
16	45.882353	36.470588	17.647059
18	8.695652	8.695652	82.608696
20	0.000000	0.000000	100.000000
21	0.000000	0.000000	100.000000
All	44.444444	33.333333	22.222222

Probability of Education for given Product "Education | Product"

pd.crosstab(aerofit['Education'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
Education
12	2.50	1.666667	0.0	1.666667
13	3.75	3.333333	0.0	2.777778
14	37.50	38.333333	5.0	30.555556
15	5.00	1.666667	0.0	2.777778
16	48.75	51.666667	37.5	47.222222
18	2.50	3.333333	47.5	12.777778
20	0.00	0.000000	2.5	0.555556
21	0.00	0.000000	7.5	1.666667

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['Education'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Education', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product ∩ Education')

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Education Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['Education'].value_counts().values,labels = aerofit['Education'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Education", x='Product')
plt.ylabel('Education Level', fontsize=12)

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Education', binwidth=1, kde=True, hue="Product")
plt.title('No of Product Purchase vs Education Level', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='Education')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='Education', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Education Level', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['Education'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Education', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['Education'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Education', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['Education'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Education for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['Education'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=5)
plt.show()

Fitness

aerofit['Fitness'].unique()

array([4, 3, 2, 1, 5], dtype=int64)

aerofit['Fitness'].value_counts()

3    97
5    31
2    26
4    24
1     2
Name: Fitness, dtype: int64

aerofit['Fitness'].value_counts(normalize=True)*100

3    53.888889
5    17.222222
2    14.444444
4    13.333333
1     1.111111
Name: Fitness, dtype: float64

Statitical Analysis

aerofit['Fitness'].describe()

count    180.000000
mean       3.311111
std        0.958869
min        1.000000
25%        3.000000
50%        3.000000
75%        4.000000
max        5.000000
Name: Fitness, dtype: float64

aerofit['Fitness'].mean()

3.311111111111111

aerofit['Fitness'].median()

3.0

aerofit['Fitness'].mode()[0]

3

aerofit.groupby('Product')["Fitness"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	2.9625	0.664540	1.0	3.0	3.0	3.0	5.0
KP481	60.0	2.9000	0.629770	1.0	3.0	3.0	3.0	4.0
KP781	40.0	4.6250	0.667467	3.0	4.0	5.0	5.0	5.0

Check for Outliers

check_outlier(aerofit, 'Fitness')['upper']

{'list': Series([], Name: Fitness, dtype: int64), 'length': 0}

check_outlier(aerofit, 'Fitness')['lower']

{'list': 14     1
 117    1
 Name: Fitness, dtype: int64,
 'length': 2}

Find Probability

Probability of a Product & Fitness across all Combination "Product ∩ Fitness"

pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
Fitness
1	0.555556	0.555556	0.000000	1.111111
2	7.777778	6.666667	0.000000	14.444444
3	30.000000	21.666667	2.222222	53.888889
4	5.000000	4.444444	3.888889	13.333333
5	1.111111	0.000000	16.111111	17.222222
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Fitness "Product | Fitness"

pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
Fitness
1	50.000000	50.000000	0.000000
2	53.846154	46.153846	0.000000
3	55.670103	40.206186	4.123711
4	37.500000	33.333333	29.166667
5	6.451613	0.000000	93.548387
All	44.444444	33.333333	22.222222

Probability of Fitness for given Product "Fitness | Product"

pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
Fitness
1	1.25	1.666667	0.0	1.111111
2	17.50	20.000000	0.0	14.444444
3	67.50	65.000000	10.0	53.888889
4	11.25	13.333333	17.5	13.333333
5	2.50	0.000000	72.5	17.222222

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['Fitness'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Fitness', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product ∩ Fitness')

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Fitness Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['Fitness'].value_counts().values,labels = aerofit['Fitness'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Fitness", x='Product')

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Fitness', binwidth=1, kde=True, hue="Product")
plt.title('No of Product Purchase vs Fitness', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='Fitness')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='Fitness', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Fitness', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=-1, ncol=3)
plt.show()

Probability Plot

(pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Fitness', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Fitness', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['Fitness'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Fitness for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=5)
plt.show()

Usage

aerofit['Usage'].unique()

array([3, 2, 4, 5, 6, 7], dtype=int64)

aerofit['Usage'].value_counts()

3    69
4    52
2    33
5    17
6     7
7     2
Name: Usage, dtype: int64

aerofit['Usage'].value_counts(normalize=True)*100

3    38.333333
4    28.888889
2    18.333333
5     9.444444
6     3.888889
7     1.111111
Name: Usage, dtype: float64

Statitical Analysis

aerofit['Usage'].describe()

count    180.000000
mean       3.455556
std        1.084797
min        2.000000
25%        3.000000
50%        3.000000
75%        4.000000
max        7.000000
Name: Usage, dtype: float64

aerofit['Usage'].mean()

3.4555555555555557

aerofit['Usage'].median()

3.0

aerofit['Usage'].mode()[0]

3

aerofit.groupby('Product')["Usage"].describe()

	count	mean	std	min	25%	50%	75%	max
Product
KP281	80.0	3.087500	0.782624	2.0	3.0	3.0	4.00	5.0
KP481	60.0	3.066667	0.799717	2.0	3.0	3.0	3.25	5.0
KP781	40.0	4.775000	0.946993	3.0	4.0	5.0	5.00	7.0

Check for Outliers

check_outlier(aerofit, 'Usage')['upper']

{'list': 154    6
 155    6
 162    6
 163    7
 164    6
 166    7
 167    6
 170    6
 175    6
 Name: Usage, dtype: int64,
 'length': 9}

check_outlier(aerofit, 'Usage')['lower']

{'list': Series([], Name: Usage, dtype: int64), 'length': 0}

Find Probability

Probability of a Product & Usage across all Combination "Product ∩ Usage"

pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize=True, margins=True)*100

Product	KP281	KP481	KP781	All
Usage
2	10.555556	7.777778	0.000000	18.333333
3	20.555556	17.222222	0.555556	38.333333
4	12.222222	6.666667	10.000000	28.888889
5	1.111111	1.666667	6.666667	9.444444
6	0.000000	0.000000	3.888889	3.888889
7	0.000000	0.000000	1.111111	1.111111
All	44.444444	33.333333	22.222222	100.000000

Probability of Product's for given Usage "Product | Usage"

pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize='index', margins=True)*100

Product	KP281	KP481	KP781
Usage
2	57.575758	42.424242	0.000000
3	53.623188	44.927536	1.449275
4	42.307692	23.076923	34.615385
5	11.764706	17.647059	70.588235
6	0.000000	0.000000	100.000000
7	0.000000	0.000000	100.000000
All	44.444444	33.333333	22.222222

Probability of Usage for given Product "Fitness | Product"

pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize='columns', margins=True)*100

Product	KP281	KP481	KP781	All
Usage
2	23.75	23.333333	0.0	18.333333
3	46.25	51.666667	2.5	38.333333
4	27.50	20.000000	45.0	28.888889
5	2.50	5.000000	30.0	9.444444
6	0.00	0.000000	17.5	3.888889
7	0.00	0.000000	5.0	1.111111

Plot the Graph

Heat Map

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['Usage'], normalize='all'),annot=True)
plt.title('Probabbility of Product ∩ Usage', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product ∩ Usage')

Descriptive Plot

plt.figure(figsize=(15,10)).suptitle("Aerofit Usage Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(aerofit['Usage'].value_counts().values,labels = aerofit['Usage'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(aerofit, y="Usage", x='Product')

plt.subplot(3, 2, 2)
sns.histplot(aerofit, x='Usage', binwidth=1, kde=True, hue="Product")
plt.title('No of Product Purchase vs Usage', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(aerofit, x='Usage')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(aerofit, x='Usage', hue='Product')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Usage', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()

Probability Plot

(pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Product & Usage', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()

(pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Product for given Usage', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()

(pd.crosstab(aerofit['Product'], aerofit['Usage'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Usage for given Product', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()

Population Plot

pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=5)
plt.show()

Customer Profile of Different Product

KP281 = aerofit.copy().loc[aerofit['Product']=='KP281']
KP481 = aerofit.copy().loc[aerofit['Product']=='KP481']
KP781 = aerofit.copy().loc[aerofit['Product']=='KP781']

Cummulative Profile

plt.figure(figsize=(20,15)).suptitle("Aerofit Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(aerofit[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(fontsize=11)

plt.subplot(4, 3, 3)
sns.countplot(aerofit, x='Gender', hue="Product")
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 6)
sns.countplot(aerofit, x='MaritalStatus', hue="Product")
plt.ylabel("Count", fontsize=11)

plt.subplot(4, 3, 7)
sns.histplot(aerofit, x="Age", binwidth=3, kde=True, hue="Product", element="step")
plt.subplot(4, 3, 8)
sns.histplot(aerofit, x="Income", binwidth=1500, kde=True, hue="Product", element="step")
plt.subplot(4, 3, 9)
sns.histplot(aerofit, x="Miles", binwidth=20, kde=True, hue="Product", element="step")

plt.subplot(4, 3, 10)
sns.histplot(aerofit, x="Education", binwidth=1, kde=True, hue="Product", element="step")
plt.subplot(4, 3, 11)
sns.countplot(aerofit, x='Fitness', hue="Product")
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 12)
sns.countplot(aerofit, x='Usage', hue="Product")
plt.ylabel("Count", fontsize=11)

plt.show()

Profile of *KP281*

KP281[['Gender', 'MaritalStatus', 'Fitness', 'Usage', 'Age']].value_counts(normalize=True)[:5]*100

Gender  MaritalStatus  Fitness  Usage  Age
Female  Partnered      2        2      25     2.5
Male    Partnered      3        3      38     2.5
Female  Single         3        4      24     2.5
        Partnered      3        2      28     2.5
Male    Single         3        4      23     2.5
dtype: float64

Statitical Analysis

KP281[['Gender', 'MaritalStatus', 'age_group', 'income_group', 'miles_group', 'education_group', 'fitness_group', 'usage_group' ]].describe()

	Gender	MaritalStatus	age_group	income_group	miles_group	education_group	fitness_group	usage_group
count	80	80	80	80	80	80	80	80
unique	2	2	7	5	6	6	5	4
top	Female	Partnered	20-25	45K-55K	80-110	16	3	3
freq	40	48	28	35	27	39	54	37

KP281[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].describe()

	Age	Income	Miles	Education	Fitness	Usage
count	80.000000	80.00000	80.000000	80.000000	80.00000	80.000000
mean	28.550000	46418.02500	82.787500	15.037500	2.96250	3.087500
std	7.221452	9075.78319	28.874102	1.216383	0.66454	0.782624
min	18.000000	29562.00000	38.000000	12.000000	1.00000	2.000000
25%	23.000000	38658.00000	66.000000	14.000000	3.00000	3.000000
50%	26.000000	46617.00000	85.000000	16.000000	3.00000	3.000000
75%	33.000000	53439.00000	94.000000	16.000000	3.00000	4.000000
max	50.000000	68220.00000	188.000000	18.000000	5.00000	5.000000

KP281[['Age', 'Income', 'Miles', 'Education', 'Usage', 'Fitness']].median()

Age             26.0
Income       46617.0
Miles           85.0
Education       16.0
Usage            3.0
Fitness          3.0
dtype: float64

Find Probability

pd.crosstab([KP281.income_group], [KP281.age_group], normalize=True, margins=True)*100

age_group	15-20	20-25	25-30	30-35	35-40	40-45	45-50	All
income_group
25K-35K	6.25	3.75	0.00	0.00	0.00	0.00	0.00	10.00
35K-45K	1.25	25.00	3.75	0.00	2.50	0.00	0.00	32.50
45K-55K	0.00	6.25	21.25	10.00	3.75	2.50	0.00	43.75
55K-65K	0.00	0.00	0.00	2.50	3.75	1.25	3.75	11.25
65K-75K	0.00	0.00	1.25	1.25	0.00	0.00	0.00	2.50
All	7.50	35.00	26.25	13.75	10.00	3.75	3.75	100.00

pd.crosstab([KP281.Fitness, KP281.Gender, KP281.MaritalStatus], [KP281.miles_group], normalize=True, margins=True)*100

		miles_group	20-50	50-80	80-110	110-140	140-170	170-200	All
Fitness	Gender	MaritalStatus
1	Male	Partnered	1.25	0.00	0.00	0.00	0.00	0.00	1.25
2	Female	Partnered	6.25	2.50	0.00	0.00	0.00	0.00	8.75
		Single	2.50	1.25	0.00	0.00	0.00	0.00	3.75
	Male	Partnered	5.00	0.00	0.00	0.00	0.00	0.00	5.00
3	Female	Partnered	0.00	12.50	8.75	1.25	0.00	0.00	22.50
		Single	0.00	5.00	3.75	1.25	0.00	0.00	10.00
	Male	Partnered	0.00	7.50	8.75	0.00	0.00	0.00	16.25
		Single	0.00	2.50	12.50	3.75	0.00	0.00	18.75
4	Female	Partnered	0.00	0.00	0.00	1.25	0.00	0.00	1.25
		Single	0.00	1.25	0.00	1.25	0.00	0.00	2.50
	Male	Partnered	0.00	0.00	0.00	2.50	1.25	0.00	3.75
		Single	0.00	0.00	0.00	2.50	1.25	0.00	3.75
5	Female	Partnered	0.00	0.00	0.00	0.00	0.00	1.25	1.25
	Male	Single	0.00	0.00	0.00	0.00	1.25	0.00	1.25
All			15.00	32.50	33.75	13.75	3.75	1.25	100.00

pd.crosstab([KP281.Fitness, KP281.Gender, KP281.MaritalStatus], [KP281.age_group], normalize=True, margins=True)*100

		age_group	15-20	20-25	25-30	30-35	35-40	40-45	45-50	All
Fitness	Gender	MaritalStatus
1	Male	Partnered	0.00	1.25	0.00	0.00	0.00	0.00	0.00	1.25
2	Female	Partnered	0.00	5.00	2.50	0.00	0.00	0.00	1.25	8.75
		Single	0.00	0.00	0.00	3.75	0.00	0.00	0.00	3.75
	Male	Partnered	1.25	1.25	1.25	1.25	0.00	0.00	0.00	5.00
3	Female	Partnered	2.50	5.00	8.75	2.50	2.50	0.00	1.25	22.50
		Single	0.00	7.50	1.25	1.25	0.00	0.00	0.00	10.00
	Male	Partnered	0.00	3.75	2.50	2.50	3.75	2.50	1.25	16.25
		Single	2.50	7.50	6.25	0.00	2.50	0.00	0.00	18.75
4	Female	Partnered	0.00	0.00	1.25	0.00	0.00	0.00	0.00	1.25
		Single	0.00	0.00	0.00	1.25	0.00	1.25	0.00	2.50
	Male	Partnered	0.00	0.00	2.50	0.00	1.25	0.00	0.00	3.75
		Single	1.25	2.50	0.00	0.00	0.00	0.00	0.00	3.75
5	Female	Partnered	0.00	1.25	0.00	0.00	0.00	0.00	0.00	1.25
	Male	Single	0.00	0.00	0.00	1.25	0.00	0.00	0.00	1.25
All			7.50	35.00	26.25	13.75	10.00	3.75	3.75	100.00

pd.crosstab([KP281.Fitness, KP281.Gender, KP281.MaritalStatus], [KP281.income_group], normalize=True, margins=True)*100

		income_group	25K-35K	35K-45K	45K-55K	55K-65K	65K-75K	All
Fitness	Gender	MaritalStatus
1	Male	Partnered	0.00	1.25	0.00	0.00	0.00	1.25
2	Female	Partnered	1.25	2.50	3.75	1.25	0.00	8.75
		Single	0.00	0.00	2.50	1.25	0.00	3.75
	Male	Partnered	0.00	2.50	2.50	0.00	0.00	5.00
3	Female	Partnered	2.50	5.00	12.50	2.50	0.00	22.50
		Single	1.25	6.25	1.25	0.00	1.25	10.00
	Male	Partnered	0.00	2.50	8.75	3.75	1.25	16.25
		Single	3.75	6.25	8.75	0.00	0.00	18.75
4	Female	Partnered	0.00	1.25	0.00	0.00	0.00	1.25
		Single	0.00	0.00	1.25	1.25	0.00	2.50
	Male	Partnered	0.00	1.25	1.25	1.25	0.00	3.75
		Single	1.25	2.50	0.00	0.00	0.00	3.75
5	Female	Partnered	0.00	1.25	0.00	0.00	0.00	1.25
	Male	Single	0.00	0.00	1.25	0.00	0.00	1.25
All			10.00	32.50	43.75	11.25	2.50	100.00

Check for Outliers

plt.figure(figsize=(15,10)).suptitle("KP281 Outliers",fontsize=14)

plt.subplot(2, 3, 1)
sns.boxplot(KP281, y="Age")
plt.subplot(2, 3, 2)
sns.boxplot(KP281, y="Income")

plt.subplot(2, 3, 3)
sns.boxplot(KP281, y="Miles")
plt.subplot(2, 3, 4)
sns.boxplot(KP281, y="Education")

plt.subplot(2, 3, 5)
sns.boxplot(KP281, y='Fitness')
plt.subplot(2, 3, 6)
sns.boxplot(KP281, y='Usage')

plt.show()

Plot the Graph

plt.figure(figsize=(20,15)).suptitle("KP281 Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(KP281[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(fontsize=11)

plt.subplot(4, 3, 3)
sns.countplot(KP281, x='Gender')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 6)
sns.countplot(KP281, x='MaritalStatus')
plt.ylabel("Count", fontsize=11)

plt.subplot(4, 3, 7)
sns.histplot(KP281, x="Age", binwidth=3, kde=True)
plt.subplot(4, 3, 8)
sns.histplot(KP281, x="Income", binwidth=1500, kde=True)
plt.subplot(4, 3, 9)
sns.histplot(KP281, x="Miles", binwidth=20, kde=True)

plt.subplot(4, 3, 10)
sns.histplot(KP281, x="Education", binwidth=1, kde=True)
plt.subplot(4, 3, 11)
sns.countplot(KP281, x='Fitness')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 12)
sns.countplot(KP281, x='Usage')
plt.ylabel("Count", fontsize=11)

plt.show()

#

Insight

• There are highest number of customer have covered 70 miles.

Profile of *KP481*

KP481[['Gender', 'MaritalStatus', 'Fitness', 'Usage', 'Age']].value_counts(normalize=True)[:5]*100

Gender  MaritalStatus  Fitness  Usage  Age
Male    Partnered      3        3      23     5.000000
                       2        2      21     3.333333
Female  Partnered      2        3      23     1.666667
Male    Partnered      2        2      45     1.666667
                                3      35     1.666667
dtype: float64

Statitical Analysis

KP481[['Gender', 'MaritalStatus', 'age_group', 'income_group', 'miles_group', 'education_group', 'fitness_group', 'usage_group' ]].describe()

	Gender	MaritalStatus	age_group	income_group	miles_group	education_group	fitness_group	usage_group
count	60	60	60	60	60	60	60	60
unique	2	2	7	5	6	6	4	4
top	Male	Partnered	20-25	45K-55K	80-110	16	3	3
freq	31	36	24	33	31	31	39	31

KP481[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].describe()

	Age	Income	Miles	Education	Fitness	Usage
count	60.000000	60.000000	60.000000	60.000000	60.00000	60.000000
mean	28.900000	48973.650000	87.933333	15.116667	2.90000	3.066667
std	6.645248	8653.989388	33.263135	1.222552	0.62977	0.799717
min	19.000000	31836.000000	21.000000	12.000000	1.00000	2.000000
25%	24.000000	44911.500000	64.000000	14.000000	3.00000	3.000000
50%	26.000000	49459.500000	85.000000	16.000000	3.00000	3.000000
75%	33.250000	53439.000000	106.000000	16.000000	3.00000	3.250000
max	48.000000	67083.000000	212.000000	18.000000	4.00000	5.000000

KP481[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].median()

Age             26.0
Income       49459.5
Miles           85.0
Education       16.0
Fitness          3.0
Usage            3.0
dtype: float64

Find Probability

pd.crosstab([KP481.income_group], [KP481.age_group], normalize=True, margins=True)*100

age_group	15-20	20-25	25-30	30-35	35-40	40-45	45-50	All
income_group
25K-35K	5.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	10.000000
35K-45K	1.666667	13.333333	0.000000	0.000000	0.000000	0.000000	0.000000	15.000000
45K-55K	0.000000	21.666667	10.000000	20.000000	1.666667	1.666667	0.000000	55.000000
55K-65K	0.000000	0.000000	1.666667	5.000000	8.333333	0.000000	1.666667	16.666667
65K-75K	0.000000	0.000000	0.000000	3.333333	0.000000	0.000000	0.000000	3.333333
All	6.666667	40.000000	11.666667	28.333333	10.000000	1.666667	1.666667	100.000000

pd.crosstab([KP481.Fitness, KP481.Gender, KP481.MaritalStatus], [KP481.miles_group], normalize=True, margins=True)*100

		miles_group	20-50	50-80	80-110	110-140	140-170	200-230	All
Fitness	Gender	MaritalStatus
1	Female	Single	1.666667	0.000000	0.000000	0.000000	0.000000	0.000000	1.666667
2	Female	Partnered	0.000000	3.333333	0.000000	0.000000	0.000000	0.000000	3.333333
		Single	1.666667	3.333333	1.666667	0.000000	0.000000	0.000000	6.666667
	Male	Partnered	5.000000	3.333333	0.000000	0.000000	0.000000	0.000000	8.333333
		Single	0.000000	1.666667	0.000000	0.000000	0.000000	0.000000	1.666667
3	Female	Partnered	0.000000	1.666667	18.333333	0.000000	0.000000	0.000000	20.000000
		Single	0.000000	3.333333	6.666667	0.000000	0.000000	0.000000	10.000000
	Male	Partnered	0.000000	5.000000	13.333333	1.666667	1.666667	0.000000	21.666667
		Single	0.000000	3.333333	6.666667	3.333333	0.000000	0.000000	13.333333
4	Female	Partnered	0.000000	0.000000	0.000000	0.000000	0.000000	1.666667	1.666667
		Single	0.000000	1.666667	0.000000	3.333333	0.000000	0.000000	5.000000
	Male	Partnered	0.000000	0.000000	3.333333	0.000000	1.666667	0.000000	5.000000
		Single	0.000000	0.000000	1.666667	0.000000	0.000000	0.000000	1.666667
All			8.333333	26.666667	51.666667	8.333333	3.333333	1.666667	100.000000

pd.crosstab([KP481.Fitness, KP481.Gender, KP481.MaritalStatus], [KP481.age_group], normalize=True, margins=True)*100

		age_group	15-20	20-25	25-30	30-35	35-40	40-45	45-50	All
Fitness	Gender	MaritalStatus
1	Female	Single	0.000000	0.000000	0.000000	1.666667	0.000000	0.000000	0.000000	1.666667
2	Female	Partnered	0.000000	1.666667	0.000000	1.666667	0.000000	0.000000	0.000000	3.333333
		Single	0.000000	5.000000	0.000000	1.666667	0.000000	0.000000	0.000000	6.666667
	Male	Partnered	0.000000	5.000000	0.000000	1.666667	0.000000	1.666667	0.000000	8.333333
		Single	0.000000	0.000000	1.666667	0.000000	0.000000	0.000000	0.000000	1.666667
3	Female	Partnered	1.666667	3.333333	3.333333	6.666667	5.000000	0.000000	0.000000	20.000000
		Single	0.000000	5.000000	3.333333	0.000000	1.666667	0.000000	0.000000	10.000000
	Male	Partnered	0.000000	10.000000	0.000000	6.666667	3.333333	0.000000	1.666667	21.666667
		Single	5.000000	3.333333	1.666667	3.333333	0.000000	0.000000	0.000000	13.333333
4	Female	Partnered	0.000000	1.666667	0.000000	0.000000	0.000000	0.000000	0.000000	1.666667
		Single	0.000000	1.666667	1.666667	1.666667	0.000000	0.000000	0.000000	5.000000
	Male	Partnered	0.000000	1.666667	0.000000	3.333333	0.000000	0.000000	0.000000	5.000000
		Single	0.000000	1.666667	0.000000	0.000000	0.000000	0.000000	0.000000	1.666667
All			6.666667	40.000000	11.666667	28.333333	10.000000	1.666667	1.666667	100.000000

pd.crosstab([KP481.Fitness, KP481.Gender, KP481.MaritalStatus], [KP481.income_group], normalize=True, margins=True)*100

		income_group	25K-35K	35K-45K	45K-55K	55K-65K	65K-75K	All
Fitness	Gender	MaritalStatus
1	Female	Single	0.000000	0.000000	0.000000	0.000000	1.666667	1.666667
2	Female	Partnered	0.000000	1.666667	1.666667	0.000000	0.000000	3.333333
		Single	0.000000	3.333333	3.333333	0.000000	0.000000	6.666667
	Male	Partnered	3.333333	0.000000	5.000000	0.000000	0.000000	8.333333
		Single	0.000000	0.000000	1.666667	0.000000	0.000000	1.666667
3	Female	Partnered	1.666667	0.000000	13.333333	5.000000	0.000000	20.000000
		Single	0.000000	1.666667	5.000000	3.333333	0.000000	10.000000
	Male	Partnered	0.000000	3.333333	13.333333	5.000000	0.000000	21.666667
		Single	3.333333	1.666667	5.000000	1.666667	1.666667	13.333333
4	Female	Partnered	1.666667	0.000000	0.000000	0.000000	0.000000	1.666667
		Single	0.000000	1.666667	3.333333	0.000000	0.000000	5.000000
	Male	Partnered	0.000000	1.666667	1.666667	1.666667	0.000000	5.000000
		Single	0.000000	0.000000	1.666667	0.000000	0.000000	1.666667
All			10.000000	15.000000	55.000000	16.666667	3.333333	100.000000

Check for Outliers

plt.figure(figsize=(15,10)).suptitle("KP481 Outliers",fontsize=14)

plt.subplot(2, 3, 1)
sns.boxplot(KP481, y="Age")
plt.subplot(2, 3, 2)
sns.boxplot(KP481, y="Income")

plt.subplot(2, 3, 3)
sns.boxplot(KP481, y="Miles")
plt.subplot(2, 3, 4)
sns.boxplot(KP481, y="Education")

plt.subplot(2, 3, 5)
sns.boxplot(KP481, y='Fitness')
plt.subplot(2, 3, 6)
sns.boxplot(KP481, y='Usage')

plt.show()

Plot the Graph

plt.figure(figsize=(20,15)).suptitle("KP481 Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(KP481[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(fontsize=11)

plt.subplot(4, 3, 3)
sns.countplot(KP481, x='Gender')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 6)
sns.countplot(KP481, x='MaritalStatus')
plt.ylabel("Count", fontsize=11)

plt.subplot(4, 3, 7)
sns.histplot(KP481, x="Age", binwidth=3, kde=True)
plt.subplot(4, 3, 8)
sns.histplot(KP481, x="Income", binwidth=1500, kde=True)
plt.subplot(4, 3, 9)
sns.histplot(KP481, x="Miles", binwidth=20, kde=True)

plt.subplot(4, 3, 10)
sns.histplot(KP481, x="Education", binwidth=1, kde=True)
plt.subplot(4, 3, 11)
sns.countplot(KP481, x='Fitness')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 12)
sns.countplot(KP481, x='Usage')
plt.ylabel("Count", fontsize=11)

plt.show()

#

Insight

• There are highest number of customer have covered 70 miles.

Profile of *KP781*

KP781[['Gender', 'MaritalStatus', 'Fitness', 'Usage', 'Age']].value_counts(normalize=True)[:5]*100

Gender  MaritalStatus  Fitness  Usage  Age
Male    Partnered      5        4      25     5.0
        Single         5        4      23     5.0
Female  Partnered      5        4      33     2.5
Male    Single         5        3      22     2.5
        Partnered      5        6      31     2.5
dtype: float64

Statitical Analysis

KP781[['Gender', 'MaritalStatus', 'age_group', 'income_group', 'miles_group', 'education_group', 'fitness_group', 'usage_group' ]].describe()

	Gender	MaritalStatus	age_group	income_group	miles_group	education_group	fitness_group	usage_group
count	40	40	40	40	40	40	40	40
unique	2	2	6	6	9	5	3	5
top	Male	Partnered	20-25	85K-95K	170-200	18	5	4
freq	33	23	17	11	12	19	29	18

KP781[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].describe()

	Age	Income	Miles	Education	Fitness	Usage
count	40.000000	40.00000	40.000000	40.000000	40.000000	40.000000
mean	29.100000	75441.57500	166.900000	17.325000	4.625000	4.775000
std	6.971738	18505.83672	60.066544	1.639066	0.667467	0.946993
min	22.000000	48556.00000	80.000000	14.000000	3.000000	3.000000
25%	24.750000	58204.75000	120.000000	16.000000	4.000000	4.000000
50%	27.000000	76568.50000	160.000000	18.000000	5.000000	5.000000
75%	30.250000	90886.00000	200.000000	18.000000	5.000000	5.000000
max	48.000000	104581.00000	360.000000	21.000000	5.000000	7.000000

KP781[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].median()

Age             27.0
Income       76568.5
Miles          160.0
Education       18.0
Fitness          5.0
Usage            5.0
dtype: float64

Find Probability

pd.crosstab([KP781.income_group], [KP781.age_group], normalize=True, margins=True)*100

age_group	20-25	25-30	30-35	35-40	40-45	45-50	All
income_group
45K-55K	20.0	2.5	0.0	0.0	0.0	0.0	22.5
55K-65K	15.0	2.5	0.0	0.0	0.0	0.0	17.5
65K-75K	5.0	2.5	0.0	0.0	0.0	0.0	7.5
75K-85K	2.5	5.0	0.0	2.5	0.0	0.0	10.0
85K-95K	0.0	15.0	7.5	0.0	5.0	0.0	27.5
95K-105K	0.0	5.0	2.5	2.5	0.0	5.0	15.0
All	42.5	32.5	10.0	5.0	5.0	5.0	100.0

pd.crosstab([KP781.Fitness, KP781.Gender, KP781.MaritalStatus], [KP781.miles_group], normalize=True, margins=True)*100

		miles_group	50-80	80-110	110-140	140-170	170-200	230-260	260-290	290-320	350-380	All
Fitness	Gender	MaritalStatus
3	Female	Single	0.0	2.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	0.0	2.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.5
		Single	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0
4	Female	Single	0.0	2.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	0.0	2.5	0.0	5.0	5.0	0.0	0.0	0.0	0.0	12.5
		Single	0.0	0.0	0.0	0.0	2.5	0.0	0.0	0.0	0.0	2.5
5	Female	Partnered	0.0	0.0	0.0	0.0	7.5	0.0	2.5	0.0	0.0	10.0
		Single	0.0	0.0	0.0	0.0	2.5	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	2.5	0.0	5.0	10.0	5.0	5.0	0.0	2.5	2.5	32.5
		Single	0.0	5.0	5.0	10.0	7.5	0.0	0.0	0.0	0.0	27.5
All			2.5	20.0	10.0	25.0	30.0	5.0	2.5	2.5	2.5	100.0

pd.crosstab([KP781.Fitness, KP781.Gender, KP781.MaritalStatus], [KP781.age_group], normalize=True, margins=True)*100

		age_group	20-25	25-30	30-35	35-40	40-45	45-50	All
Fitness	Gender	MaritalStatus
3	Female	Single	0.0	2.5	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	2.5	0.0	0.0	0.0	0.0	0.0	2.5
		Single	2.5	2.5	0.0	0.0	0.0	0.0	5.0
4	Female	Single	2.5	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	5.0	7.5	0.0	0.0	0.0	0.0	12.5
		Single	0.0	0.0	0.0	0.0	2.5	0.0	2.5
5	Female	Partnered	2.5	5.0	2.5	0.0	0.0	0.0	10.0
		Single	2.5	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	10.0	10.0	5.0	2.5	0.0	5.0	32.5
		Single	15.0	5.0	2.5	2.5	2.5	0.0	27.5
All			42.5	32.5	10.0	5.0	5.0	5.0	100.0

pd.crosstab([KP781.Fitness, KP781.Gender, KP781.MaritalStatus], [KP781.income_group], normalize=True, margins=True)*100

		income_group	45K-55K	55K-65K	65K-75K	75K-85K	85K-95K	95K-105K	All
Fitness	Gender	MaritalStatus
3	Female	Single	0.0	0.0	2.5	0.0	0.0	0.0	2.5
	Male	Partnered	0.0	2.5	0.0	0.0	0.0	0.0	2.5
		Single	2.5	0.0	0.0	0.0	2.5	0.0	5.0
4	Female	Single	2.5	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	0.0	5.0	2.5	0.0	2.5	2.5	12.5
		Single	0.0	0.0	0.0	0.0	2.5	0.0	2.5
5	Female	Partnered	0.0	2.5	0.0	0.0	5.0	2.5	10.0
		Single	2.5	0.0	0.0	0.0	0.0	0.0	2.5
	Male	Partnered	2.5	2.5	2.5	7.5	7.5	10.0	32.5
		Single	12.5	5.0	0.0	2.5	7.5	0.0	27.5
All			22.5	17.5	7.5	10.0	27.5	15.0	100.0

Check for Outliers

plt.figure(figsize=(15,10)).suptitle("KP781 Outliers",fontsize=14)

plt.subplot(2, 3, 1)
sns.boxplot(KP781, y="Age")
plt.subplot(2, 3, 2)
sns.boxplot(KP781, y="Income")

plt.subplot(2, 3, 3)
sns.boxplot(KP781, y="Miles")
plt.subplot(2, 3, 4)
sns.boxplot(KP781, y="Education")

plt.subplot(2, 3, 5)
sns.boxplot(KP781, y='Fitness')
plt.subplot(2, 3, 6)
sns.boxplot(KP781, y='Usage')

plt.show()

Plot the Graph

plt.figure(figsize=(20,15)).suptitle("KP781 Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(KP781[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(fontsize=11)

plt.subplot(4, 3, 3)
sns.countplot(KP781, x='Gender')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 6)
sns.countplot(KP781, x='MaritalStatus')
plt.ylabel("Count", fontsize=11)

plt.subplot(4, 3, 7)
sns.histplot(KP781, x="Age", binwidth=3, kde=True)
plt.subplot(4, 3, 8)
sns.histplot(KP781, x="Income", binwidth=1500, kde=True)
plt.subplot(4, 3, 9)
sns.histplot(KP781, x="Miles", binwidth=20, kde=True)

plt.subplot(4, 3, 10)
sns.histplot(KP781, x="Education", binwidth=1, kde=True)
plt.subplot(4, 3, 11)
sns.countplot(KP781, x='Fitness')
plt.ylabel("Count", fontsize=11)
plt.subplot(4, 3, 12)
sns.countplot(KP781, x='Usage')
plt.ylabel("Count", fontsize=11)

plt.show()

#

Insight

• There are highest number of customer have covered 70 miles.

Questions

Q0. What is the probability of a male customer buying a KP781 treadmill?

product_gender = pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize='index', margins=True)*100
product_gender

Product	KP281	KP481	KP781
Gender
Female	52.631579	38.157895	9.210526
Male	38.461538	29.807692	31.730769
All	44.444444	33.333333	22.222222

product_gender['KP781']['Male']

31.73076923076923

#

Insight

• There is 31.73% chance a male customer will purchase KP781

Q1. What is the total count of each product present in the dataset?

Products = aerofit.groupby("Product")["Age"].describe().T

Products[:1]

Product	KP281	KP481	KP781
count	80.0	60.0	40.0

Products[:1]/1.8

Product	KP281	KP481	KP781
count	44.444444	33.333333	22.222222

Insight

• KP781 40 with 22.22% probability.
• KP781 60 with 33.33% probability.
• KP781 80 with 44.44% probability.

Q2. Describe the Age & Gender distribution of all the customers?

aerofit.groupby("Gender")["Age"].describe()

	count	mean	std	min	25%	50%	75%	max
Gender
Female	76.0	28.565789	6.342104	19.0	24.00	26.5	33.0	50.0
Male	104.0	28.951923	7.377978	18.0	23.75	26.0	34.0	48.0

sns.boxplot(aerofit, x='Gender', y='Age')
plt.show()

aerofit.groupby('Gender').Age.plot(kind='hist', alpha=0.6)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper right', borderaxespad=5)
plt.show()

aerofit.groupby('Gender')['Age'].mean()

Gender
Female    28.565789
Male      28.951923
Name: Age, dtype: float64

male_age = aerofit.loc[aerofit['Gender']=='Male']['Age']
female_age = aerofit.loc[aerofit['Gender']=='Female']['Age']

t,p = ttest_ind(male_age, female_age, alternative='greater')
p

0.3567980420783239

#

Insight

• Their is a small differance between mean Age of Male & Female.
• also, p-vale of T-Test of the 2 Group come out to be 35% (considering Male age is higher)
• when following 95% Confidence Interval. So, we failed to prove that Male age are Higher then Female Age. amoung Customers.

Q3. Top 3 features having the highest coorelatios with the Product column. and Why?

top_corr = aerofit.copy()
def add_product_as_column(x):
    return 1 if(x == 'KP281') else (2 if(x == 'KP481') else (3 if(x == 'KP781') else 0))
top_corr['product'] = top_corr.apply(lambda row: add_product_as_column(row['Product']),axis=1)

def add_product_columns(x):
    x['KP281'] = np.where((x['Product'] == 'KP281'), 1, 0)
    x['KP481'] = np.where((x['Product'] == 'KP481'), 1, 0)
    x['KP781'] = np.where((x['Product'] == 'KP781'), 1, 0)
    return x
top_corr = add_product_columns(top_corr)

top3_correlations = top_corr[['Age', 'Income', 'Miles', 'Education', 'Fitness', 'Usage', 'product', 'KP281', 'KP481', 'KP781']].corr()[['product', 'KP781', 'KP481', 'KP281']][:6]

top3_correlations.sort_values('product', ascending=False, inplace=True)

sns.heatmap(top3_correlations, annot=True)
plt.show()

#

Insight

• Product Have higher co-relation with Income, Fitness & Miles.
• For KP781 we have observed same co-relation.
• But, for KP481 Top 3 co-relation are Age, Education & Income.
• But, for KP281 Top 3 co-relation are Age, Education & Usage.

As, we know KP781 is the expansive & better one, mostly prefered by Athletes or Fitness enthusiast, whos also have higher Income. But, for KP481, KP281 people with good Education, who are health consious, want to have a Treadmill but can't efford a expansive one.

Q4. Were there any outliers present in the Data? If yes, suggest suitable method for their treatment?

plt.figure(figsize=(24,12)).suptitle("Aerofit Outliers",fontsize=14)
plt.subplots_adjust(right=1)


plt.subplot(2, 6, 1)
sns.boxplot(aerofit, y='Usage')
plt.title('Usage', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 2)
sns.boxplot(aerofit, y='Usage', x='Product')
plt.title('Usage', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.subplot(2, 6, 3)
sns.boxplot(aerofit, y="Education")
plt.title('Education', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 4)
sns.boxplot(aerofit, y="Education", x='Product')
plt.title('Education', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.subplot(2, 6, 5)
sns.boxplot(aerofit, y='Fitness')
plt.title('Fitness', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 6)
sns.boxplot(aerofit, y='Fitness', x='Product')
plt.title('Fitness', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.subplot(2, 6, 7)
sns.boxplot(aerofit, y="Age")
plt.title('Age', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 8)
sns.boxplot(aerofit, y="Age", x='Product')
plt.title('Age', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.subplot(2, 6, 9)
sns.boxplot(aerofit, y="Income")
plt.title('Income', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 10)
sns.boxplot(aerofit, y="Income", x='Product')
plt.title('Income', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.subplot(2, 6, 11)
sns.boxplot(aerofit, y="Miles")
plt.title('Miles', fontsize=11)
plt.ylabel('', fontsize=12)
plt.yticks([])
plt.subplot(2, 6, 12)
sns.boxplot(aerofit, y="Miles", x='Product')
plt.title('Miles', fontsize=11)
plt.xlabel('', fontsize=12)
plt.ylabel('', fontsize=12)

plt.show()

Using Z-Score

aerofit[np.abs(zscore(aerofit['Age']))>2]

	Product	Gender	MaritalStatus	Age	Income	Miles	Education	Fitness	Usage	age_group	income_group	miles_group	education_group	fitness_group	usage_group
75	KP281	Male	Partnered	43	53439	66	16	3	3	40-45	45K-55K	50-80	16	3	3
76	KP281	Female	Single	44	57987	75	16	4	3	40-45	55K-65K	50-80	16	4	3
77	KP281	Female	Partnered	46	60261	47	16	2	3	45-50	55K-65K	20-50	16	2	3
78	KP281	Male	Partnered	47	56850	94	16	3	4	45-50	55K-65K	80-110	16	3	4
79	KP281	Female	Partnered	50	64809	66	16	3	3	45-50	55K-65K	50-80	16	3	3
138	KP481	Male	Partnered	45	54576	42	16	2	2	40-45	45K-55K	20-50	16	2	2
139	KP481	Male	Partnered	48	57987	64	16	3	2	45-50	55K-65K	50-80	16	3	2
177	KP781	Male	Single	45	90886	160	16	5	5	40-45	85K-95K	140-170	16	5	5
178	KP781	Male	Partnered	47	104581	120	18	5	4	45-50	95K-105K	110-140	18	5	4
179	KP781	Male	Partnered	48	95508	180	18	5	4	45-50	95K-105K	170-200	18	5	4

Using IQR (Inner Quartile Range)

age_outlier = check_outlier(aerofit, 'Age')
income_outlier = check_outlier(aerofit, 'Income')
miles_outlier = check_outlier(aerofit, 'Miles')
education_outlier = check_outlier(aerofit, 'Education')
fitness_outlier = check_outlier(aerofit, 'Fitness')
usage_outlier = check_outlier(aerofit, 'Usage')

print("Age: \t\t => Lower outlier: {}  => Upper outlier: {}" .format(age_outlier['lower']['length'], age_outlier['upper']['length']))
print("Income: \t => Lower outlier: {}  => Upper outlier: {}" .format(income_outlier['lower']['length'], income_outlier['upper']['length']))
print("Miles: \t\t => Lower outlier: {}  => Upper outlier: {}" .format(miles_outlier['lower']['length'], miles_outlier['upper']['length']))
print("Education: \t => Lower outlier: {}  => Upper outlier: {}" .format(education_outlier['lower']['length'], education_outlier['upper']['length']))
print("Fitness: \t => Lower outlier: {}  => Upper outlier: {}" .format(fitness_outlier['lower']['length'], fitness_outlier['upper']['length']))
print("Usage: \t\t => Lower outlier: {}  => Upper outlier: {}" .format(usage_outlier['lower']['length'], usage_outlier['upper']['length']))
# print("%d, %d" %(age_outlier['lower']['length'], age_outlier['upper']['length']))

Age: 		 => Lower outlier: 0  => Upper outlier: 5
Income: 	 => Lower outlier: 0  => Upper outlier: 19
Miles: 		 => Lower outlier: 0  => Upper outlier: 13
Education: 	 => Lower outlier: 0  => Upper outlier: 4
Fitness: 	 => Lower outlier: 2  => Upper outlier: 0
Usage: 		 => Lower outlier: 0  => Upper outlier: 9

#

Insight

• Yes, There is Outlier Present in DataSet for All of the Continuous Values, like
  • Age: => Lower outlier: 0 => Upper outlier: 5
  • Income: => Lower outlier: 0 => Upper outlier: 19
  • Miles: => Lower outlier: 0 => Upper outlier: 13
  • Education: => Lower outlier: 0 => Upper outlier: 4
  • Fitness: => Lower outlier: 2 => Upper outlier: 0
  • Usage: => Lower outlier: 0 => Upper outlier: 9

When, we remove these outliers it might effect credebility of Product specific analysis. Because Some of the product dependent on the datas.

Q5. Marital Status Implies no significant information on the usages of different TreadMills? (T/F)

sns.heatmap(pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize=True),annot=True)
plt.title('Probabbility of Product ∩ Marital Status', fontsize=12)
plt.show()

sns.heatmap(pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize='columns'),annot=True)
plt.title('Probabbility of Marital Status | Product', fontsize=12)
plt.show()

sns.heatmap(pd.crosstab(aerofit['Product'], aerofit['MaritalStatus'], normalize='columns'),annot=True)
plt.title('Probabbility of Product | Marital Status', fontsize=12)
plt.show()

#

Insight

• From Above Probability Heatmap, observed that for each product given marital status have nearly same probability.

Thus, Statement is True

Q6. The variance of Income in lower ages is smaller as compare to the variance in higher ages. *In Statistics , this is known as*

low_income_age = aerofit.copy().loc[aerofit['Age']<aerofit['Age'].mean()][['Income', "Age"]]
high_income_age = aerofit.copy().loc[aerofit['Age']>aerofit['Age'].mean()][['Income', "Age"]]

sns.scatterplot(aerofit, x='Income', y='Age', hue='Product')
plt.title("Income vs Age", fontsize=12)
plt.show()

low_income_age['Income'].var() < high_income_age['Income'].var()

True

low_income_age['Income'].std() < high_income_age['Income'].std()

True

#

Insight

• Here Variance & Standard Daviation in low Age Income Group is less then Higher Age Income Group.
• Thus, It results a funnel shape & It's known as Heteroscedasticity

Q7. What proportion of woman have brought the KP781 TreadMill? Provide reson of Answar.

product_gender = pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize='index', margins=True)*100
product_gender

Product	KP281	KP481	KP781
Gender
Female	52.631579	38.157895	9.210526
Male	38.461538	29.807692	31.730769
All	44.444444	33.333333	22.222222

product_gender['KP781']['Female']

9.210526315789473

#

Insight

• There is 9.21% chance a male customer will purchase KP781

Q8. Distinguish between Customer Profiles for KP281 and KP481 TreadMill.

Probability of Product's for given Age "Product | Age"

sns.heatmap(pd.crosstab(aerofit['age_group'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Age', fontsize=12)
plt.ylabel('Age')
plt.show()

Probability of Product's for given Income "Product | Income"

sns.heatmap(pd.crosstab(aerofit['income_group'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Income', fontsize=12)
plt.ylabel('Income')
plt.show()

Probability of Product's for given Miles "Product | Miles"

sns.heatmap(pd.crosstab(aerofit['miles_group'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Miles', fontsize=12)
plt.ylabel('Miles')
plt.show()

Probability of Product's for given Education "Product | Education"

sns.heatmap(pd.crosstab(aerofit['Education'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Education', fontsize=12)
plt.show()

Probability of Product's for given Fitness "Product | Fitness"

sns.heatmap(pd.crosstab(aerofit['Fitness'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Fitness', fontsize=12)
plt.show()

Probability of Product's for given Usage "Product | Usage"

sns.heatmap(pd.crosstab(aerofit['Usage'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Usage', fontsize=12)
plt.show()

Probability of Product's for given Gender "Product | Gender"

sns.heatmap(pd.crosstab(aerofit['Gender'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Gender', fontsize=12)
plt.show()

Probability of Product's for given MaritalStatus "Product | Marital Status"

sns.heatmap(pd.crosstab(aerofit['MaritalStatus'], aerofit['Product'], normalize='index')[['KP281', 'KP481']], annot=True)
plt.title('Probabbility of Product | Marital Status', fontsize=12)
plt.show()

Q9. The overall Probability of purchase for KP281, KP481 & KP781 TreadMill is __ , ___ , ___.

aerofit.groupby('Product')['Product'].count()/1.80

Product
KP281    44.444444
KP481    33.333333
KP781    22.222222
Name: Product, dtype: float64

Q10. Give conditions when you will and when you 'll not recomended KP781 TreadMill to a Customer?

#

**When to recomend KP781**

• Male's
• Age between 20-30.
• Income with 90K.
• Who inteded or alredy covered 150-200 Miles.
• Have Education between 16-19.
• Have Fitness of level5.
• Have Usages level of 4-5.

**When not to recomend KP781**

• Females's
• Age above 35.
• Income below 75K.
• Have Fitness level less than 3.
• Have Usages level less than 4.

Multi Variate Analysis

Find Probability & Coorelation

Probability of Product's for given Gender & Marital Status "Product | (Gender ∩ Matial Status)"

p_pord_gend_marital= pd.crosstab([aerofit['Gender'], aerofit['MaritalStatus']], aerofit['Product'], normalize='index', margins=True)*100
# p_pord_gend_marital

sns.heatmap(p_pord_gend_marital, annot=True)
plt.title('Probabbility of Product | (Gender ∩ Matial Status)', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product | (Gender ∩ Matial Status)')

Probability of Product's for given Fitness & Usage "Product | (Fitness ∩ Usage)"

p_pord_fit_usag= pd.crosstab([aerofit['Fitness'], aerofit['Usage']], aerofit['Product'], normalize='index', margins=True)*100
# p_pord_fit_usag

sns.heatmap(p_pord_fit_usag)
plt.title('Probabbility of Product | (Fitness ∩ Usage)', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product | (Fitness ∩ Usage)')

Probability of Product's for given Fitness & Age Group "Product | (Fitness ∩ Miles)"

p_pord_fit_age_group= pd.crosstab([aerofit['Fitness'], aerofit['Age']], aerofit['Product'], normalize='index', margins=True)*100
# p_pord_fit_age_group

sns.heatmap(p_pord_fit_age_group)
plt.title('Probabbility of Product | (Fitness ∩ Age Group)', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product | (Fitness ∩ Age Group)')

Probability of Product's for given Fitness & Mile Group "Product | (Fitness ∩ Mile Group)"

p_pord_fit_mile_group= pd.crosstab([aerofit['Fitness'], aerofit['Miles']], aerofit['Product'], normalize='index', margins=True)*100
# p_pord_fit_mile_group

sns.heatmap(p_pord_fit_mile_group)
plt.title('Probabbility of Product | (Fitness ∩ Miles Group)', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product | (Fitness ∩ Miles Group)')

Probability of Product's for given Usage & Income Group "Product | (Fitness ∩ Mile Group)"

p_pord_usage_income_group= pd.crosstab([aerofit['Usage'], aerofit['income_group']], aerofit['Product'], normalize='index', margins=True)*100
# p_pord_usage_income_group

sns.heatmap(p_pord_usage_income_group)
plt.title('Probabbility of Product | (Usage ∩ Income Group)', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Product | (Usage ∩ Income Group)')

Find Correlation

sns.boxplot(x="MaritalStatus", y="Age", hue="Product", data=aerofit)

<Axes: xlabel='MaritalStatus', ylabel='Age'>

sns.boxplot(x="MaritalStatus", y="Income", hue="Product", data=aerofit)

<Axes: xlabel='MaritalStatus', ylabel='Income'>

sns.boxplot(x="Gender", y="Income", hue="Product", data=aerofit)

<Axes: xlabel='Gender', ylabel='Income'>

sns.boxplot(x="Fitness", y="Age", hue="Product", data=aerofit)

<Axes: xlabel='Fitness', ylabel='Age'>

sns.boxplot(x="Fitness", y="Income", hue="Product", data=aerofit)

<Axes: xlabel='Fitness', ylabel='Income'>

sns.lineplot(x="Age", y="Education", hue="Product", data=aerofit)

<Axes: xlabel='Age', ylabel='Education'>

END